{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing Chipotle's Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time we are going to pull data directly from the internet.\n",
    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "\n",
    "# set this so the graphs open internally\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called chipo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "chipo = pd.read_csv('https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv', sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. See the first 10 entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>item_name</th>\n",
       "      <th>choice_description</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Fresh Tomato Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Izze</td>\n",
       "      <td>[Clementine]</td>\n",
       "      <td>$3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Nantucket Nectar</td>\n",
       "      <td>[Apple]</td>\n",
       "      <td>$3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Tomatillo-Green Chili Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Tomatillo-Red Chili Salsa (Hot), [Black Beans...</td>\n",
       "      <td>$16.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...</td>\n",
       "      <td>$10.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Side of Chips</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$1.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Tomatillo Red Chili Salsa, [Fajita Vegetables...</td>\n",
       "      <td>$11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Soft Tacos</td>\n",
       "      <td>[Tomatillo Green Chili Salsa, [Pinto Beans, Ch...</td>\n",
       "      <td>$9.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Pinto...</td>\n",
       "      <td>$9.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  quantity                              item_name  \\\n",
       "0         1         1           Chips and Fresh Tomato Salsa   \n",
       "1         1         1                                   Izze   \n",
       "2         1         1                       Nantucket Nectar   \n",
       "3         1         1  Chips and Tomatillo-Green Chili Salsa   \n",
       "4         2         2                           Chicken Bowl   \n",
       "5         3         1                           Chicken Bowl   \n",
       "6         3         1                          Side of Chips   \n",
       "7         4         1                          Steak Burrito   \n",
       "8         4         1                       Steak Soft Tacos   \n",
       "9         5         1                          Steak Burrito   \n",
       "\n",
       "                                  choice_description item_price  \n",
       "0                                                NaN     $2.39   \n",
       "1                                       [Clementine]     $3.39   \n",
       "2                                            [Apple]     $3.39   \n",
       "3                                                NaN     $2.39   \n",
       "4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...    $16.98   \n",
       "5  [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...    $10.98   \n",
       "6                                                NaN     $1.69   \n",
       "7  [Tomatillo Red Chili Salsa, [Fajita Vegetables...    $11.75   \n",
       "8  [Tomatillo Green Chili Salsa, [Pinto Beans, Ch...     $9.25   \n",
       "9  [Fresh Tomato Salsa, [Rice, Black Beans, Pinto...     $9.25   "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chipo.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Create a histogram of the top 5 items bought"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "item_name\n",
       "Chicken Bowl           761\n",
       "Chicken Burrito        591\n",
       "Chips and Guacamole    506\n",
       "Steak Burrito          386\n",
       "Canned Soft Drink      351\n",
       "Name: quantity, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chipo.groupby('item_name')['quantity'].sum().sort_values(ascending=False).head(5).plot(kind='bar')\n",
    "plt.xlabel('Items')\n",
    "plt.ylabel('Number of Times Ordered')\n",
    "plt.title('Most ordered Chipotle\\'s Items')\n",
    "chipo.groupby('item_name')['quantity'].sum().sort_values(ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chipo.item_name.value_counts(ascending=False).head(5).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                Chips and Fresh Tomato Salsa\n",
       "1                                        Izze\n",
       "2                            Nantucket Nectar\n",
       "3       Chips and Tomatillo-Green Chili Salsa\n",
       "4                                Chicken Bowl\n",
       "                        ...                  \n",
       "4617                            Steak Burrito\n",
       "4618                            Steak Burrito\n",
       "4619                       Chicken Salad Bowl\n",
       "4620                       Chicken Salad Bowl\n",
       "4621                       Chicken Salad Bowl\n",
       "Name: item_name, Length: 4622, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the Series of the names\n",
    "x = chipo.item_name\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Chips and Fresh Tomato Salsa': 110,\n",
       "         'Izze': 20,\n",
       "         'Nantucket Nectar': 27,\n",
       "         'Chips and Tomatillo-Green Chili Salsa': 31,\n",
       "         'Chicken Bowl': 726,\n",
       "         'Side of Chips': 101,\n",
       "         'Steak Burrito': 368,\n",
       "         'Steak Soft Tacos': 55,\n",
       "         'Chips and Guacamole': 479,\n",
       "         'Chicken Crispy Tacos': 47,\n",
       "         'Chicken Soft Tacos': 115,\n",
       "         'Chicken Burrito': 553,\n",
       "         'Canned Soda': 104,\n",
       "         'Barbacoa Burrito': 91,\n",
       "         'Carnitas Burrito': 59,\n",
       "         'Carnitas Bowl': 68,\n",
       "         'Bottled Water': 162,\n",
       "         'Chips and Tomatillo Green Chili Salsa': 43,\n",
       "         'Barbacoa Bowl': 66,\n",
       "         'Chips': 211,\n",
       "         'Chicken Salad Bowl': 110,\n",
       "         'Steak Bowl': 211,\n",
       "         'Barbacoa Soft Tacos': 25,\n",
       "         'Veggie Burrito': 95,\n",
       "         'Veggie Bowl': 85,\n",
       "         'Steak Crispy Tacos': 35,\n",
       "         'Chips and Tomatillo Red Chili Salsa': 48,\n",
       "         'Barbacoa Crispy Tacos': 11,\n",
       "         'Veggie Salad Bowl': 18,\n",
       "         'Chips and Roasted Chili-Corn Salsa': 18,\n",
       "         'Chips and Roasted Chili Corn Salsa': 22,\n",
       "         'Carnitas Soft Tacos': 40,\n",
       "         'Chicken Salad': 9,\n",
       "         'Canned Soft Drink': 301,\n",
       "         'Steak Salad Bowl': 29,\n",
       "         '6 Pack Soft Drink': 54,\n",
       "         'Chips and Tomatillo-Red Chili Salsa': 20,\n",
       "         'Bowl': 2,\n",
       "         'Burrito': 6,\n",
       "         'Crispy Tacos': 2,\n",
       "         'Carnitas Crispy Tacos': 7,\n",
       "         'Steak Salad': 4,\n",
       "         'Chips and Mild Fresh Tomato Salsa': 1,\n",
       "         'Veggie Soft Tacos': 7,\n",
       "         'Carnitas Salad Bowl': 6,\n",
       "         'Barbacoa Salad Bowl': 10,\n",
       "         'Salad': 2,\n",
       "         'Veggie Crispy Tacos': 1,\n",
       "         'Veggie Salad': 6,\n",
       "         'Carnitas Salad': 1})"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use the Counter class from collections to create a dictionary with keys(text) and frequency\n",
    "letter_counts = Counter(x)\n",
    "letter_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chips and Fresh Tomato Salsa</th>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Izze</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nantucket Nectar</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chips and Tomatillo-Green Chili Salsa</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicken Bowl</th>\n",
       "      <td>726</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         0\n",
       "Chips and Fresh Tomato Salsa           110\n",
       "Izze                                    20\n",
       "Nantucket Nectar                        27\n",
       "Chips and Tomatillo-Green Chili Salsa   31\n",
       "Chicken Bowl                           726"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert the dictionary to a DataFrame\n",
    "df = pd.DataFrame.from_dict(letter_counts, orient='index')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chicken Bowl           726\n",
       "Chicken Burrito        553\n",
       "Chips and Guacamole    479\n",
       "Steak Burrito          368\n",
       "Canned Soft Drink      301\n",
       "Name: 0, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort the values from the top to the least value and slice the first 5 items\n",
    "df = df[0].sort_values(ascending = False)[:5]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create the plot\n",
    "df.plot(kind='bar')\n",
    "\n",
    "# Set the title and labels\n",
    "plt.xlabel('Items')\n",
    "plt.ylabel('Number of Times Ordered')\n",
    "plt.title('Most ordered Chipotle\\'s Items')\n",
    "\n",
    "# show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Create a scatterplot with the number of items orderered per order price\n",
    "#### Hint: Price should be in the X-axis and Items ordered in the Y-axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>item_name</th>\n",
       "      <th>choice_description</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Fresh Tomato Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Izze</td>\n",
       "      <td>[Clementine]</td>\n",
       "      <td>$3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Nantucket Nectar</td>\n",
       "      <td>[Apple]</td>\n",
       "      <td>$3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Tomatillo-Green Chili Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Tomatillo-Red Chili Salsa (Hot), [Black Beans...</td>\n",
       "      <td>$16.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  quantity                              item_name  \\\n",
       "0         1         1           Chips and Fresh Tomato Salsa   \n",
       "1         1         1                                   Izze   \n",
       "2         1         1                       Nantucket Nectar   \n",
       "3         1         1  Chips and Tomatillo-Green Chili Salsa   \n",
       "4         2         2                           Chicken Bowl   \n",
       "\n",
       "                                  choice_description item_price  \n",
       "0                                                NaN     $2.39   \n",
       "1                                       [Clementine]     $3.39   \n",
       "2                                            [Apple]     $3.39   \n",
       "3                                                NaN     $2.39   \n",
       "4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...    $16.98   "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chipo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2.39\n",
       "1        3.39\n",
       "2        3.39\n",
       "3        2.39\n",
       "4       16.98\n",
       "        ...  \n",
       "4617    11.75\n",
       "4618    11.75\n",
       "4619    11.25\n",
       "4620     8.75\n",
       "4621     8.75\n",
       "Name: item_price, Length: 4622, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a list of prices\n",
    "chipo.item_price = [float(value[1:-1]) for value in chipo.item_price] # strip the dollar sign and trailing space\n",
    "chipo.item_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>quantity</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>11.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>16.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>12.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>21.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>13.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1830</th>\n",
       "      <td>2</td>\n",
       "      <td>23.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1831</th>\n",
       "      <td>3</td>\n",
       "      <td>12.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1832</th>\n",
       "      <td>2</td>\n",
       "      <td>13.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1833</th>\n",
       "      <td>2</td>\n",
       "      <td>23.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1834</th>\n",
       "      <td>3</td>\n",
       "      <td>28.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1834 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          quantity  item_price\n",
       "order_id                      \n",
       "1                4       11.56\n",
       "2                2       16.98\n",
       "3                2       12.67\n",
       "4                2       21.00\n",
       "5                2       13.70\n",
       "...            ...         ...\n",
       "1830             2       23.00\n",
       "1831             3       12.90\n",
       "1832             2       13.20\n",
       "1833             2       23.50\n",
       "1834             3       28.75\n",
       "\n",
       "[1834 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# then groupby the orders and sum\n",
    "orders = chipo.groupby('order_id').sum()\n",
    "orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creates the scatterplot\n",
    "# plt.scatter(orders.quantity, orders.item_price, s = 50, c = 'green')\n",
    "plt.scatter(x = orders.item_price, y = orders.quantity, s = 50, c = 'green')\n",
    "\n",
    "# Set the title and labels\n",
    "plt.xlabel('Order Price')\n",
    "plt.ylabel('Items ordered')\n",
    "plt.title('Number of items ordered per order price')\n",
    "plt.ylim(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. BONUS: Create a question and a graph to answer your own question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
